The incidence of gestational diabetes mellitus among women with polycystic ovary syndrome: a meta-analysis of longitudinal studies

Background Previous studies have shown that polycystic ovary syndrome is a predictor of gestational diabetes mellitus, but we do not know exactly how many polycystic ovary syndrome patients may develop gestational diabetes mellitus. Currently, the incidence of gestational diabetes mellitus among women with polycystic ovary syndrome varies greatly across studies, ranged from 4.12% to 59.50%. Besides, many factors have been found to be related to the incidence of gestational diabetes mellitus among women with polycystic ovary syndrome, but the results among different studies are not consistent. The possible causes of inconsistencies between the current estimates were unclear. This review aimed at exploring the pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome, summarizing possible causes of the inconsistencies in the current estimates, try to provide a reference for prevention of gestational diabetes mellitus and polycystic ovary syndrome in the future. Methods Systematic searches of different databases (including EMBASE, Web of Science, MEDLINE, The Cochrane Library, CNKI and PubMed) were conducted for studies published until 31 May 2021. Statistical analyses were performed using R software, the pooled incidence of gestational diabetes mellitus among polycystic ovary syndrome patients was combined using random effects model. Cochrane’s “Tool to Assess Risk of Bias in Cohort Studies” was used for quality assessment. Results Twenty-two longitudinal studies were included. A total of 24,574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus. The pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was 20.64%, with a 95% CI of 14.64% to 28.30%. In the meta-regression model, several variables including age, area, quality score and sample size were suggested as significant sources of heterogeneity, accounted for 77.57% of the heterogeneity across studies. Conclusions Evidence in this review suggests that gestational diabetes mellitus were common among women with polycystic ovary syndrome. More research is needed to found effective interventions for preventing gestational diabetes mellitus among women with polycystic ovary syndrome. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-022-04690-3.

The items of quality assessment Item (i) Can we be confident in the assessment of exposure (ie, the predictor variables)?
(ii) Were the exposed and non-exposed cohorts selected from the same population?
(iii) Can we be confident that the outcome of interest was not present at the start of the study?
(iv) Did the statistical analysis adjust for the confounding variables?
(v) Can we be confident in the assessment of the presence or absence of confounding factors?
(vi) Can we be confident in the assessment of the outcome?
(vii) Was the follow-up of the cohorts adequate? Note: Symbols ++ = definitely yes; + = probably/mostly yes; -= probably/mostly no; --= definitely no  Background: Previous studies have shown that PCOS is a predictor of GDM, but we do not know exactly how many PCOS patients may develop GDM. Currently, the incidence of GDM among women with PCOS varies greatly across studies, ranged from 4.12% to 59.50%. In addition, many factors have been reported to be associated with the incidence of GDM among women with PCOS, but the results are not consistent in different studies. The possible causes of the inconsistencies in the current estimates were unclear. This review aimed at examining the pooled incidence of GDM among women with PCOS, summarizing possible vulnerability factors of GDM among women with PCOS, try to provide a reference for prevention of GDM and PCOS in the future.
Methods: Systematic searches of databases were conducted for literature published until 31 May 2021. Statistical analyses were performed using R software, the pooled incidence was combined using random effects model. Cochrane's "Tool to Assess Risk of Bias in Cohort Studies" was used for quality assessment.
Results: Twenty-two longitudinal studies were included. A total of 24574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus. The pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was 20.64%, with a 95% CI of 14.64% to 28.30%. In the meta-regression model, several variables including age, area, quality score and sample size were found as significant sources of heterogeneity, accounted for 77.57% of the heterogeneity across studies.

Introduction
Objectives 4 This review aimed at examining the pooled incidence of GDM among women with PCOS, summarizing possible vulnerability factors of GDM among women with PCOS, try to provide a reference for prevention of GDM and PCOS in the future.

Protocol and registration 5
This review was reported in accordance with the PRISMA guideline and MOOSE guidelines.

Eligibility criteria 6
Studies were included if they meet the following criteria: (1) the study was longitudinal observational study; (2) the participants were women with polycystic ovary syndrome; (3) information about incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was provided; (4) the full article was written in English or Chinese. Studies were excluded if (1) the report was a meta-analysis, review, conference abstract, comments, or protocol. Data extraction was conducted independently in pairs by trained researchers who used standardized data extraction forms. Two reviewers (QZY and DQ) checked the titles, abstracts and full-texts of the initial search results independently. Data were extracted on first author, country/area, publication year, sample size, mean age, mean BMI, percentage of overweight/obese patients, percentage of primigravida, percentage of smoking participants, mean age of participants, instruments used to identify GDM, incidence of GDM, and quality score of the included studies. Any discrepancies that emerged in these procedures were discussed and resolved by involving a third reviewer (XL).

Data collection process 10
Data extraction was conducted independently in pairs by trained researchers who used standardized data extraction forms. Two reviewers (QZY and DQ) checked the titles, abstracts and full-texts of the initial search results independently. Any discrepancies that emerged in these procedures were discussed and resolved by involving a third reviewer (XL).

Data items 11
Data were extracted on first author, country/area, publication year, sample size, mean age, mean BMI, percentage of overweight/obese patients, percentage of primigravida, percentage of smoking participants, instruments used to identify GDM, incidence of GDM, and quality score of the included studies.

Risk of bias in individual studies
12 Two independent reviewers (RZL and YXH) used the established guidelines, Cochrane's "Tool to Assess Risk of Bias in Cohort Studies", to evaluate the methodological quality of the included studies, which has been widely used to evaluate observational studies Methods Summary measures 13 Incidence of GDM Methods

14
When data were available for three or more papers, incidence of gestational diabetes mellitus was combined (32). When there were 4 or more studies available, quantitative subgroup analysis was conducted (33). All the statistical analyses in this study were performed using the "meta" (4.13-0) and "metafor" package (2.4-0) of R version 4.0.0. Heterogeneity between the included studies was evaluated by Cochran's Q test and quantified by the I2 statistic. When the results of I2 greater than 50%, means moderate heterogeneity (33). As the authors expected considerable heterogeneity, pooled incidence of gestational diabetes mellitus was calculated with the random effects model (34). The pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was combined using Logit transformation method by a random effects model in the current study. In order to compare the incidence of gestational diabetes mellitus from different studies, subgroup analysis was conducted. Previous research indicated that subgroup analyses should be interpreted with caution, we planned a priori to limit our subgroup analyses to a limited number of baseline characteristics including area, mean age, mean BMI, percentage of overweight/obese patients, percentage of primigravida, percentage of smoking patients, sample size, and quality score (34). The difference between those subgroups was examined using the Cochran's Q chi-square tests. Mixed-model meta-regression analyses were performed by using Freeman-Tukey double arcsine method to explore potential moderators on the heterogeneity. Publication bias was investigated by funnel plot and Egger's test. To evaluate the consistency of the results, sensitivity analysis was performed. In this study, sensitivity analyses Methods were planned a priori for the primary analyses set by excluding studies one by one. All the statistical tests were 2-sided, with a significance threshold of P < 0.05.
Page 1 of 2 In order to compare the incidence from different studies (such as age, area, diagnostic method, BMI etc.), we conducted subgroup metaanalysis. The difference between subgroups was examined using the Cochran's Q chi-square tests. Mixed-model meta-regression analyses were performed by using Freeman-Tukey double arcsine method to explore potential moderators on the heterogeneity.

Study selection 17
As reported in Fig. 1, a total of 616 references were identified. Among those references, 95 duplicates were removed. By screening titles and abstracts, 445 irrelevant articles were excluded. A total of 76 potentially relevant full-text articles were independently assessed based on the selection criteria. Further, 54 studies were excluded because of the following reasons: duplicate articles or results (n = 8); review or conference abstract (n = 4); did not provide data on incidence of gestational diabetes mellitus among women with polycystic ovary syndrome (n = 32); not observational study (n = 7); unable to locate full text (n = 3). Finally, 22 eligible studies were included in this review. See Fig. 1 for the details.

Study characteristics
18  Table S2 and Table S3.

Risk of bias within studies 19
From the 22 studies, 9 (40.91%) studies were rated as high or acceptable quality and 13 (59.09%) were rated as low quality. Details of the methodological quality assessments of all 22 studies are showed in Additional File 2.

Results of 20
There were 22 studies reported incidence of gestational diabetes Results individual studies mellitus among women with polycystic ovary syndrome. The forest plot in Fig. 2 depicts the details. A total of 24574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus.

21
There were 22 studies reported incidence of gestational diabetes mellitus among women with polycystic ovary syndrome. The forest plot in Fig. 2 depicts the details. A total of 24574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus. The random effects model was used to determine the pooled incidence (Q= 1997.85, I2 = 98.80%, P < 0.001), the pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was 20.64%, with a 95% CI of 14.64% to 28.30%.

Risk of bias across studies 22
Funnel plot of publication bias is presented in Fig. 3. The funnel plot of publication bias is basically symmetric, but publication bias cannot be ruled out, so Egger's test was conducted. The results of the Egger's test showed that publication bias was not found in this study (t = 0.362, p = 0.721). When each study was excluded one-by-one, the recalculated combined results did not change significantly. The pooled incidence of GDM among PCOS patients ranged from 19.31% (95% CI: 13.78%-26.37%) to 22.44% (95% CI: 16.44%-26.86%), and the I2 statistic has ranged from 98.00% to 98.90%. The results in the current study indicate that no individual study significantly influenced the overall results. See Fig  S1 for the details of sensitivity analysis.

Additional analysis 23
The details of subgroup analyses are presented in Table 2. Significant differences in the incidence of gestational diabetes mellitus between different age was found (Q=8.08, P = 0.040). The results indicated that older polycystic ovary syndrome patients showed higher incidence of gestational diabetes mellitus, younger participants (with a mean age ≤25) showed lowest incidence of gestational diabetes mellitus (6.98%). Although no significant difference in the incidence of gestational diabetes mellitus between different BMI group was observed (20.05% vs. 24.74%; Q= 5.31, P = 0.021), the results indicated that studies with higher percentage of overweight/obese patients showed higher incidence of gestational diabetes mellitus (18.74% vs. 14.34% vs. 28.30% vs. 40.37%; Q= 59.09, P < 0.001). In addition, we found that studies with higher percentage of primigravida (> 30%) showed higher incidence of gestational diabetes mellitus (31.04% vs. 55.39%; Q= 97.84, P < 0.001). Also, studies with higher percentage of smoking patients (>10%) showed higher incidence of gestational diabetes mellitus (13.87% vs. 39.02%; Q= 4.05, P = 0.044) The pooled incidence of gestational diabetes mellitus among polycystic ovary syndrome patients in the European region, the Western Pacific Results region, the America region, the South-East Asia region and the Eastern Mediterranean region was 19.06%, 22.33%, 34.38%, 14.34% and 20.88%, respectively. No significant differences in the incidence of gestational diabetes mellitus between different region was found (Q= 5.33, P = 0.255). Furthermore, the pooled incidence of gestational diabetes mellitus among patients in the high-income region and the upper-middle-income region was 19.74% and 21.65%, respectively. No significant differences in the incidence of gestational diabetes mellitus between different income classification group was found (Q= 0.08, P = 0.783). Additionally, significant difference in the incidence of gestational diabetes mellitus between studies with different sample size was observed, articles with higher sample size (>300) showed lower incidence of gestational diabetes mellitus (27.40% vs. 14.02%; Q= 4.26, P = 0.038). For studies with different quality, the incidence of gestational diabetes mellitus in high-quality researches is lower than that of low-quality researches. However, the difference was not significant (26.05% vs. 14.54%; Q= 3.03, P = 0.081). Table 3 showed the results of meta-regression analyses. Due to too many missing data on the percentage of overweight/obese patients, percentage of primigravida, percentage of smoking patients, we were unable to include those variables in the meta-regression model. Bivariate meta-regression suggested that higher incidence estimates reported in studies with small sample (β = −0.19, p = 0.041). Specifically, sample size accounted for 20.15% of the heterogeneity across studies. Also, higher incidence estimates reported in studies which used ADA criteria as assessment tool (β = −0.21, p = 0.043). Specifically, sample size accounted for 22.11% of the heterogeneity across studies. Besides, area (β = −0.04, p = 0.676), quality score (β = -0.08, p = 0.422), mean BMI (β = 0.02, p = 0.513) and mean age (β = -0.06, p = 0.516) and were not significant moderators. Of the multivariate model, area (β = -0.24, p = 0.011), quality score (β = -0.12, p = 0.039), sample size (β = -0.39, p < 0.001) and mean age (β = -0.08, p = 0.028) were found as significant moderators for heterogeneity (P < 0.05), accounted for 77.57% of the heterogeneity across studies.

Summary of evidence 24
A total of 24574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus. The pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was 20.64%, with a 95% CI of 14.64% to 28.30%. In the meta regression analyses, several variables including age, area, quality score and sample size were found as significant sources of heterogeneity, accounted for 77.57% of the heterogeneity across studies.

Discussion
Limitations 25 Firstly, we excluded papers were not written in English or Chinese. Besides, although subgroup analyses were conducted to control many moderating factors for the pooled incidence of GDM among PCOS patients, heterogeneity remained in this review. It is reported that heterogeneity is difficult to avoid in meta-analysis of epidemiological surveys (68), which suggesting the need for caution when drawing inferences about estimates of GDM among PCOS patients. Additionally, although this review included relevant studies across 11 countries, most of the eligible studies were from high income countries, no study was conducted in low-income country. Considering the inconsistency of the health care environment and economic status worldwide, more incidence studies in low-income countries are needed to understand the panorama of GDM among PCOS patients. Also, we noticed that the included studies covering a vast range of clinical and diagnostic criteria and practice changes (58). It is possible that the pooled incidence of GDM among PCOS patients was influenced by the changes of threshold value to identify GDM. Thus, we think ongoing surveillance is essential.

Conclusions 26
A total of 24574 women with polycystic ovary syndrome were identified in the 22 articles, of which 4478 were reported with gestational diabetes mellitus. The pooled incidence of gestational diabetes mellitus among women with polycystic ovary syndrome was 20.64%, with a 95% CI of 14.64% to 28.30%. In the meta regression analyses, several variables including age, area, quality score and sample size were found as significant sources of heterogeneity, accounted for 77.57% of the heterogeneity across studies. Further research is needed to explore more possible risk factors for GDM and identify effective strategies for preventing GDM among PCOS patients.  We planned to contacted authors for unpublished studies during the screening process when necessary, no such abstracts and unpublished studies appears in articles that meet the inclusion criteria at last.  Description of any contact with authors Not applicable (All articles that meet the inclusion criteria have complete data for pooled prevalence) Reporting of methods should include  Description of relevance or appropriateness of studies assembled for assessing the hypothesis to be tested Detailed inclusion and exclusion criteria were described in the methods section.

FUNDING
 Rationale for the selection and coding of data Two reviewers (QZY and DQ) checked the titles, abstracts and full-texts of the initial search results independently. Data were extracted on first author, country/area, publication year, sample size, mean age, mean BMI, percentage of overweight/obese patients, percentage of primigravida, percentage of smoking participants, instruments used to identify GDM, incidence of GDM, and quality score of the included studies. Any discrepancies that emerged in these procedures were discussed and resolved by involving a third reviewer (XL).  Assessment of confounding In order to compare the incidence of gestational diabetes mellitus from different studies, subgroup analysis was conducted. Previous research indicated that subgroup analyses should be interpreted with caution, we planned a priori to limit our subgroup analyses to a limited number of baseline characteristics including area, mean age, mean BMI, percentage of overweight/obese patients, percentage of primigravida, percentage of smoking patients, sample size, and quality score.
 Assessment of study quality, including blinding of quality assessors; stratification or regression on possible predictors of study results Two independent reviewers (RZL and YXH) used the established guidelines, Cochrane's "Tool to Assess Risk of Bias in Cohort Studies", to evaluate the methodological quality of the included studies, which has been widely used to evaluate observational studies.  Assessment of heterogeneity Heterogeneity of the studies were explored within two types of study designs using Cochrane's Q test of heterogeneity and I 2 statistic that provides the relative amount of variance of the summary effect due to the between-study heterogeneity.  Description of statistical methods in sufficient detail to be replicated Description of methods of meta-analyses, sensitivity analyses, meta-regression and assessment of publication bias are detailed in the methods.  Provision of appropriate tables and graphics We included 1 flow chart,1 summary  Indication of statistical uncertainty of findings 95% confidence intervals were presented with all summary estimates, I 2 values and results of sensitivity analyses Reporting of discussion should include  Quantitative assessment of bias The results of the Egger's test showed that publication bias was not found in this study and the sensitivity analysis showed that no individual study significantly influenced the overall results. However, the observed heterogeneity should be noticed.

 Justification for exclusion
We excluded studies that not write in English or Chinese, which was a limitation in this review.  Assessment of quality of included studies We discussed the results of the subgroup analyses, and potential reasons for the observed heterogeneity. Reporting of conclusions should include  Consideration of alternative We noted that the variations in the incidence may be due to explanations for observed results true population differences, or to differences in quality of studies, sample size, etc.  Generalization of the conclusions Evidence suggests that the incidence of GDM were very common among PCOS patients. Further research is needed to explore more possible risk factors for GDM and identify effective strategies for preventing GDM among PCOS patients.  Guidelines for future research During the process of screening data, we found that there were relatively few data on incidence of GDM among PCOS patients. Of the 22 included studies, 13 (59.09%) were rated as low quality and 59.09% of the included studies with a sample size ≤ 300. Thus, we think a large multicenter prospective study using a single validated measure of GDM and measuring possible confounding factors in randomly selected PCOS patients is needed in the future, which would provide a more accurate estimate of GDM among PCOS patients. Currently, the results of population-based studies of dietary or combined lifestyle measures have not indicated too much improvements in the risk of developing GDM. Besides, those trials involving physical activity programs have yielded conflicting results. Given the great potential for reducing the disease burden of PCOS patients, future research should continue to identify interventions that can be easily implemented in patients with PCOS, especially during their preconception period. Additionally, due to lack of data in many subgroups, we were unable to perform meta regression analysis for some possible confounders, such as socioeconomic status, family history of GDM, physical activity, drinking and diet habit. Thus, there might be a considerable amount of uncertainty regarding the pooled incidence of GDM among PCOS patients. Future research should, therefore, explore more potential risk factors for GDM among PCOS patients, especially genetic background as well as health-related behavior or other concomitant chronic diseases.  Disclosure of funding source This research was supported by the Health Commission of Hunan Province (Grant NO: B2017167) and Hunan Pharmaceutical Association (Grant NO: Hn201707). The funding agency did not take part in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript